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380 Reilly Fankhauser et al.
[125]. The authors utilized a targeted mass by parallel reaction monitoring strategy,
which combines the use of the quadripole and Orbitrap platforms resulting in a sensitive
and specific method for quantitative proteomics. They measured PD-1, PD-L1, and
PD-L2 in preimmunotherapy and postimmunotherapy treatment melanoma biopsies
and compared results to IHC assessments of the same tissue. Measurement of PD-L1 with
the two techniques was largely in concordance; however, MS provided the molar quantities of molecules, whereas IHC only determined the percentages of cells that exceeded a
staining intensity threshold. Resultantly, some samples that reported high PD-L1 expression through IHC analysis had some of the lowest molar expression of PD-L1 as determined by MS. The authors also identified N-glycosylated forms of PD-L1 that were not
detectable with IHC. Lastly, they determined that PD-L2 levels were comparable to
PD-L1, suggesting another potential target. They concluded that a combination of
IHC and mass spectrometry may be the most appropriate strategy for evaluating and
improving immunotherapies.
MS has some disadvantages, including the cost of instrumentation, difficult experimental designs, and complex data analysis, which restrict its clinical utility, though it
has been incorporated in some clinical pipelines [126–128]. Currently, few laboratories
have the equipment or expertise to apply these tools at a clinical scale.
We have discussed numerous technologies that can be used in exploratory experiments to discover novel biomarkers to predict responses to therapy, including Olink
PEA panels, the Somalogic Somascan, and MS. These approaches are often coupled
to or followed by validation using lower-throughput approaches that require a priori
knowledge about the target of interest. Next, we will discuss some of these tools, including enzyme-linked immunosorbent assays (ELISA), flow cytometry (FCM), and cytometry by time of flight (CyTOF).
2.10 Flow cytometry
As we have seen in many of the studies mentioned thus far, FCM is often used to analyze
tumor and immune cell types and their activation states in conjunction with other proteomic assays. Often, the larger assays that can analyze hundreds to thousands of protein
targets and inflammatory cytokines at once are utilized in an exploratory effort to determine changes in immune response, while FCM is used to investigate targets for which we
have some level of a priori knowledge. This often includes immunophenotyping using
canonical markers. The largest advancements in FCM technologies are in increasing
the number of colors that can be investigated simultaneously. Some instruments and
reagents can evaluate 28 colors, though 12–15 colors are more typical [129]. We will
not make FCM technology a focus of this chapter; however, we would like to point
to some interesting examples of how FCM has been used in conjunction with other
proteomic-based assays to investigate responses to immunotherapy [91,93–97,130–134].
2.11 Mass cytometry; cytometry by time of flight (CyTOF)
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Advances in mass cytometry (MC) technology have altered the landscape of biomarker
discovery and proteomics. FCM has been a field standard in profiling the proteome for
years; however, this technology has significant limitations. The most notable is the minimal number of parameters that can be analyzed at one time. FCM utilizes fluorescently
tagged antibodies to target proteins of interest. There is a significant risk of spectral overlap due to adjacent emission ranges of fluorophores, which consequentially limits
researchers to fewer than 28 simultaneous fluorophores per staining panel [135]. Alternatively, mass cytometry technology allows for the simultaneous analysis of >40 markers
utilizing metal-tagged isotopes—a significant improvement over FCM [136,137]. The
benefit of using metal-tagged antibodies is that when spillover between isotopes exists,
it is typically less than 3% [138].
In CyTOF, single-cell suspensions are stained with an antibody tag that is covalently
conjugated to a metal-chelating polymer [139]. These antibodies can target extra- and
intracellular targets of interest utilizing common fixation and permeabilization techniques. The single-cell suspensions are then fed into the mass cytometer, where the cells
are nebulized into tiny droplets and injected into an argon plasma that strips electrons
from the metals to generate ions. The ion mixture is then filtered through an electrostatic
field to separate ions. These ions are then identified and quantified at the detector, yielding expression data for the targets of interest [140–142].
Since its commercial inception, CyTOF has been successfully employed by
researchers to depict various aspects of the proteome. Specific to immunotherapies, Krieg
et al. analyzed PBMCs from patients with metastatic melanoma using CyTOF to discern
markers for responders vs nonresponders in patients receiving anti-PD-1 therapy [143].
They identified several immune cell signatures that may explain response vs nonresponse.
They discovered that at baseline, responders had increased expression of activation and
migration markers (HLA-DR, ICAM-1) on classical monocytes (CD14
nonresponders. After therapy initiation, responding patients had increased frequencies
of central memory T and NKT cells with a more activated T cell compartment
(CTLA-4
+
, TNF-α+, PD-1+, Grz-B+, and IL-2+). Responders also had a reduction
in peripheral T cells, suggesting these cells are more effective at migrating to the tumor.
Taken together, the authors utilize the multiparameter ability of CyTOF to identify signatures in responders and nonresponders before and after anti-PD-1 treatment. The
higher peripheral myeloid frequencies were seen in responders, due to T cells moving
from the blood to the tumor as previously observed [144], may be used as a prognostic
marker to determine if patients will respond to anti-PD-1 therapy.
In another example, Gide et al. combined transcriptomic and proteomic data to ana-
lyze tumor tissue to understand the mechanism of response in anti-PD-1 monotherapy
and combined anti-CTLA-4/anti-PD-1 immunotherapy [145]. For CyTOF, they used a
+
CD16-) than
381Proteomic biomarker technology for cancer immunotherapy
382 Reilly Fankhauser et al.
43 marker panel to quantify T cell differentiation and transcription factors, and major
immune subtypes. They were able to cluster three discrete T cell groups. The authors
identified an interesting subgroup of CD45RO
+
EOMES+T cells in responders undergoing combination therapy. This cell subgroup also expressed CD69 and CD103,
markers of activation and tissue residency, as well as TBET and HLA-DR that indicate
an effector memory phenotype. Ultimately, the data suggest that TBET and EOMES
may be useful biomarkers of immunotherapy response and provide insight into how these
therapies work and how to make them more effective.
There are some limitations to CyTOF. Data analysis can be challenging and relies on
manual gating, similar to FCM, which can introduce inter-lab discrepancies [146]. This
emphasizes the need for reliable, optimized markers that can aid in the gating process.
Sample preparation can also be challenging as heavy metal contamination needs to be
avoided, yet these are common ingredients in many basic reagents. CyTOF is slower than
FCM (250–500 cells/second vs thousands of cells/second with FCM), increasing
experiment time. Analysis with CyTOF also destroys the analyte—cells cannot be sorted
out for downstream analysis or outgrowth as one can with FACS [136]. Despite these
limitations, CyTOF remains a useful proteomic tool for biomarker discovery and therapy
monitoring. Continued technological improvements, including the expansion of the
number of markers, will make CyTOF more clinically relevant.
2.12 Western blotting and ELISA
Western blotting and enzyme-linked immunosorbent assays (ELISA) are ubiquitous laboratory techniques that employ the specificity of antibody-based detection for visualizing
proteins. Traditional ELISAs are popular due to their speed, sensitivity, specificity, simplicity, and cost, though they are highly limited in throughput, which makes the discovery of
additional biomarkers difficult [147,148]. Traditional ELISAs have primarily been used in
efforts to validate previously identified biomarkers. ELISAs have been used to individually
monitor lactate dehydrogenase, soluble PD-1, soluble PD-L1, and angiopoietin-2, which
may be predictive of response to immunotherapies in various cancers [149–152].
While their application is limited in biomarker discovery due to the low-throughput
nature of these technologies, technical advances have made these assays advantageous for
comprehensive immunotherapy monitoring. In contrast to the traditional ELISA, the
ELISpot investigates secreted cytokines and growth factors at single-cell resolution.
Stimulated cells are incubated in wells coated with capture antibodies, enabling the fixation of the target analyte as it is locally secreted. Subsequent incubation with the appropriate detection antibody, enzyme conjugate, and chromogenic substrate will develop
spots corresponding to the localized analyte secretion, following a “one spot, one cell”
principle. This correlation enables the identification of rare immune subsets within a
diverse cell population, quantifying both the number of activated cells (number of spots)
and the relative analyte concentration (spot intensity) [153]. The ELISpot has gained
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popularity for monitoring activated Cytotoxic T lymphocyte frequency and function
in cancer vaccine trials, where INFγ, Granzyme B, or perforin are utilized as biomarkers
of T cell activation [154,155]. Given that cytokine secretion does not definitively corroborate effector function or antitumor cytotoxicity, the ELISpot readout is a better indicator of immune activation rather than of definitive immunotherapy response [155].
Further, spontaneous variation in cytokine production by immune subsets throughout
immunotherapy may also complicate the interpretation of the ELISpot assay, necessitating longitudinal ELISpot testing [154].
As with the case of traditional ELISAs, western blotting has not been widely used to
discover biomarkers that predict response to immunotherapy due to the low-throughput
nature of the technology. Recently, western blotting technology has been expanded
upon to interrogate individual cells at the protein level. Single-cell western blotting
(scWestern) employs cell deposition, lysis, protein electrophoresis, immobilization,
and blotting, all on microwell-bearing polyacrylamide-coated slides [156]. The utiliza-
tion of PAGE protein separation allows greater distinction between protein isoforms than
by immunoblotting alone; however, the use of gradient gel pores to improve size discrimination can concomitantly inhibit antibody transport into the gel and subsequent
protein detection [157]. Much like traditional western blotting, scWesterns are additionally limited by antibody sensitivity in detecting low abundance proteins and confounding
background signals, while also prone to protein loss during lysis [156]. Still, this technique
allows a multiplexed assessment of cells from a small sample population, enabling a closer
inspection of heterogeneous immune subpopulations for candidate biomarkers. Notably,
this technique can be used to profile T cells and monitor their response to immunotherapies, as previously discussed [158]. While analyzing cellular heterogeneity using
scWestern technology is an exciting and novel approach, the analysis of single activation
states may be similarly profiled using a panel of antibodies with conventional FCM—
equipment that has been extensively validated over decades of research that many health
centers already have access to.
383Proteomic biomarker technology for cancer immunotherapy
3. Proteomic analysis of immune-related adverse events
As previously discussed, immune-related adverse events (irAEs) are a common or
even fatal phenomenon associated with ICB administration. As researchers search for
novel approaches to increase the efficacy of these immunotherapies and increase the
number of patients that derive clinical benefit from them, we are likely to see an increased
rate of irAEs if no steps are taken to understand and mitigate this risk. One proposed
approach to attaining improved responses involves combining the highly cytotoxic properties of targeted chemotherapies with immunotherapies [159]. Targeted chemotherapies
384 Reilly Fankhauser et al.
have a high response rate, but relapse is common [159]. Immunotherapies tend to bring
about long-lasting responses, but with a lower response rate [159]. Thus, one attractive
approach is to combine the broadly effective properties of chemotherapies with the durable responses of immunotherapies. The combination of targeted BRAF plus MEK inhibition in patients with BRAF-mutant melanoma with ICB therapies has been
investigated in clinical trials; however, this was associated with dramatic side effects
[160]. In the patients on the triplet therapy arm (ipilimumab, dabrafenib, and trametinib),
the authors reported an alarming toxicity profile; two cases of colitis-associated with
colon perforation were noted in the first seven patients, requiring extensive downstream
workup with steroids and even surgery, even though the doses of dabrafenib and trametinib were kept low.
With the alarming rate of irAEs as high as it is already in clinical practice and the likelihood that they become increasingly common, it is extremely important that much
research effort is placed on trying to understand the biological drivers of these events
and what we can do to predict and/or prevent their occurrence. Proteomic-based technologies are uniquely poised to investigate the drivers of irAEs and identify biomarkers to
predict their occurrence in patients.
3.1 SERPA & SEREX for antigen/autoantibody detection
Interrogating patient sera for potential biomarkers is a minimally invasive method that is
well suited for longitudinal monitoring. Serum proteome analysis (SERPA) is an immunoblotting technique that can offer a comprehensive look at the humoral immune
response by identifying autoantibodies to tumor-associated or other antigens [161].
We will first discuss the identification of autoantibodies as implicated in responses to
immunotherapy, then discuss autoantibody detection in the setting of irAEs. In the
SERPA method, protein lysate from either patient tumors or cultured cells is separated
by isoelectric focusing and gel electrophoresis. Following membrane transfer, the protein
array is blotted with patient serum, allowing any reactive autoantibodies the opportunity
to bind their cognate antigens. Protein bands identified by secondary antibodies are
excised from a complementary gel and identified by mass spectrometry to determine
the antigen of interest. SERPA uniquely enables the recognition of intact proteins and
their posttranslational modifications as tumor-associated antigen candidates [162]. Autoantibodies identified by SERPA have been implicated as diagnostic biomarkers for neoplasia with the potential to differentiate between malignant phenotypes [163,164].
Autoantibodies against tumor-associated antigens have been identified in many cancer
types, including pancreatic, ovarian, and prostate [165,166]. Notably, melanoma patients
with autoantibodies against NY-ESO-1 showed a greater benefit to treatment with
ipilimumab [167]. The major disadvantage of utilizing autoantibodies as biomarkers
are variation in their longitudinal expression. Research has shown that autoantibody titers
are differentially influenced by chemo and radiation therapies, leading to speculation as to
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the definitive effect of immunotherapy on the modulation of autoantibody titers [162].
An alternative to SERPA, the SErological analysis of antigens by REcombinant
EXpression cloning (SEREX) method involves constructing a cDNA library from either
fresh tumor specimens/cultured cells or obtaining a commercially available cDNA expression library. These cDNA expression libraries are cloned into lambda phage expression
vectors and are transfected into E. coli. Recombinant proteins expressed via the bacterial
host are transferred onto a nitrocellulose membrane, then the membranes are incubated
with patient serum. IgG antibodies that are bound to expression constructs are identified
with an enzyme-conjugated secondary antibody specific to IgG. Positive clones are subcloned, and the nucleotide sequence of the cDNA insert is determined [168,169].
The SEREX method has been used to identify drivers of irAEs. Tahir et al. analyzed
plasma samples from patients who developed ICB-induced hypophysitis or pneumonitis
and used the SEREX method to discover autoantibodies [170]. They found that patients
who developed hypophysitis shared autoantibodies against GNAL and ITM2B, while
patients who developed pneumonitis shared autoantibodies against CD74. They validated the findings of their discovery cohort by performing ELISAs on sera from a validation cohort. The authors also demonstrated that CD74 is expressed in lung tissue, and
GNAL and ITM2B are expressed in pituitary tissue. They also demonstrated that CD74
expression was increased in patients that developed pneumonitis, thus elucidating its role
as a potential biomarker for the development of irAEs.
This study was limited in the size of its discovery and validation cohorts; thus, it may
be missing other essential autoantibodies. It is also important to note that the SEREX
method relies on cDNA libraries either constructed from fresh tumor specimens and
cloned into lambda phage expression vectors or, as in this case, predefined cDNA libraries
of the human brain and lung cancer. Thus, unless this approach can be performed on a
cDNA library prepared from fresh tissue taken from the site of an irAE, it may be difficult
to identify patient-specific epitopes or neoepitopes that are driving an irAE. Determining
the epitope using DNA sequencing loses the opportunity to analyze posttranslational
modifications; therefore, the SERPA method may be more appropriate to profile
patient-specific epitopes that drive irAEs comprehensively. That said, this is an influential
demonstration of a platform for discovering and validating autoantibodies that drive ICBinduced irAEs.
Autoantibodies are by no means the only biomarkers implicated in irAEs. Johnson
et al., utilized the nanostring DSP to investigate a case of a patient who developed fatal
checkpoint inhibitor-induced encephalitis [171]. Analysis of inflamed and uninflamed
neural tissue showed a high degree of cytotoxic activation, including increased expression
of CD4, CD8, CD3, Ki67, PD-L1, GZMB, and CD45RO and a small increase in the
B cell markers CD19 and CD20 in the affected region [171]. Teachey et al. identified
high pretreatment blood levels of IL-6 are strongly associated with the development
385Proteomic biomarker technology for cancer immunotherapy
386 Reilly Fankhauser et al.
of cytokine release syndrome following CAR T cell therapy [172]. Some researchers predict that cytokines may be excellent biomarkers for immunotherapy toxicity [173],
though these results need further validation. There is extreme diversity regarding the
mechanism of action and clinical presentation of immunotherapy-induced irAEs
[4,5,174–176]. Therefore it is highly likely that a comprehensive understanding of pre-
dictive biomarkers for the development of immunotherapy-induced irAEs will require a
multiplicity of analyses ranging from peripheral cytokines to autoantibodies to the microbiome. Advancements in proteomic biomarker technologies and appropriately designed
clinical trials to evaluate them will be critical to developing a comprehensive understanding of irAEs and their biomarkers.
4. Discussion and future directions
Due to the complex mechanism of how immunotherapies induce an antitumor
response, it makes sense that a single biomarker will be insufficient for predicting response
to therapies [177,178]. Advancements in predictions for treatment response will likely
rely upon multiomic analyses investigating proteomic, transcriptomic, and genomic targets in tumor specimens and other tissue types, as seen by the explosion of papers that
utilize multiple methodologies to identify biomarkers in various diseases [92,179–184].
Another promising research avenue to improve patient outcomes to immunotherapy
comes from investigating the role of the microbiome [185–187]. Proteomic analyses,
without or without DNA sequencing, can be used to interrogate microbiota diversity
and function during and after fecal transplantation with organisms known to be associated
with an improved response to immunotherapy [119,188].
The increased number of analyses being performed on tumor sections, blood samples,
and fecal specimens to deliver personalized medicine may have several important implications. First, the technologies should be integrated with one another to conserve tissue.
Some techniques of tissue acquisition, like fine needle aspiration, can result in scarce specimen retrieval. Therefore each analysis, albeit genomic, transcriptomic, or proteomic,
must be tissue sparing. One tissue-sparing approach is the use of BD Biosciences Rhapsody platform or the 10X Genomics CITE-Seq Technology, which allows for simultaneous evaluation of the transcriptome with cell-surface protein makers [158,189].
Second, the rise of multiple tests opens up the possibility of a two-tiered health system
based on patient ability to afford advanced proteomic, transcriptomic, and genetic testing.
Those who can afford more extensive testing may have better cancer outcomes than
those who cannot. Therefore, vigilant focus should be placed on (A) cost-effective test
strategies and (B) ensuring that more advanced testing strategies such as MS and MIBITOF are implemented into clinical practice in such a way that they are broadly accessible.
As we uncover more about the underlying biological drivers of cancers and targeted
therapeutic strategies, we will discover novel biomarkers and tools to predict responses to
treatments. A pipeline for how these biomarkers may be brought into clinical practice is
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demonstrated in Fig. 2. The process begins with discovery, which can be performed with
many of the broad panels and assays that we have discussed in this chapter, including
SERPA and SEREX to detect autoantibodies, MS, CyTOF, mIHC, CycIF, Nanostring
DSP, quantitative multiplexed fluorescence immunohistochemistry, IMC, MIBI, PEA,
or the Somalogic SomaScan. The putative targets discovered with broad screening assays
are then verified using assays with high sensitivity and specificity to ensure analytic validity. This is often performed by analyzing the target in a larger validation cohort with conventional, low-throughput, but high sensitivity assays such as ELISA, ELISPOT, western
blotting, IHC, IF, and flow cytometry. Lastly, the putative biomarkers are clinically validated to ensure that they can adequately stratify patients into groups, aid in clinical decision making and diagnosis, and improve patient outcomes. It is of paramount importance
387Proteomic biomarker technology for cancer immunotherapy
Fig. 2 Framework for developing a candidate biomarker and how it may be facilitated by proteomic
technologies.
388 Reilly Fankhauser et al.
to ensure that randomized, controlled clinical trials back the implementation of these biomarkers into clinical practice and that diagnostic tests are performed with the utmost
scrutiny. Diagnostic tests and the laboratories that perform them should undergo continuous external review and validation to minimize variability in testing results or the interpretation of results. Proteomic-based biomarkers are no exception as these complex
methods for evaluating clinical specimens become more commonplace in clinical
practice.
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